Exoplanets are any planet that’s outside of our solar system. There’s a myriad of ways to detect these planets, one of which is transit-timing variation. A planet is transiting when it passes in front of its star. If there is more than one planet in the system, there will be variations in the times at which it transits. This is due to gravitational perturbations from the planets on each other. This project focuses on the TRAPPIST-1 system, and more specifically Planet h of this system. We have the transit-timing variation (ttv) data for 7 planets in this system (named Planet b-h). We use an N-Body model to simulate the transit times, and compare this to the data to find the physical parameters of the system. An N-body model of 6 planets, and the data of 6 planets (excluding the Planet h’s data), is used to find the parameters. Then an N-body model of 7 planets is used with the data of the 6 planets. In comparing these models, we predict that the 7 body model will fit better. Although the 7th planet’s data is excluded, the signal from this planet will still be present in the ttvs of the other planets, due to the gravitational perturbations. If the model does fit better using 7 bodies in the N-body simulation but only data from 6 planets, we can see that the transit timing variations are strong enough to tell us that there is a 7th planet. This can be translated to other systems that have similar transit timing variation to see if adding another planet will create a better fit.

Previous studies show that the high prevalence of body image concern and disordered eating among Western women precipitates from the promotion of an unrealistically thin body ideal. Exposure to thin-idealized images on social media has been found to increase body dissatisfaction and perpetuate internalization of anti-fat attitudes. The present research examined whether implicit anti-fat attitudes can be internalized without ever being visually exposed to body shapes. Preliminary results from a sample of blind women (N = 18) in Israel revealed that implicit anti-fat attitudes in blind females are comparable to those of sighted controls. The purpose of this study was to expand on these original findings by administering a novel auditory weight-bias Implicit Association Test (IAT) to larger samples of blind women (N = 30) and sighted controls (N = 30) across the United States. Participants completed an auditory IAT in which they paired words describing thin bodies and words describing overweight bodies with either negative or positive words in congruent and incongruent blocks. Afterwards, they answered self-report questionnaires to evaluate body dissatisfaction, pursuit of the thin ideal, and perceived pressure from family, friends, significant others, and the media to attain a thin body. In our analysis, I helped compare reaction times of blind and sighted female participants. Implicit anti-fat bias was reflected as slower reaction times when words describing overweight bodies were paired with positive words compared to when they were paired with negative words. If results of the present study replicate the preliminary results, we will develop stronger evidence towards the existence of non-visual influences that contribute to thin-ideal internalization. Additionally, these results would demonstrate that internalization of the thin-ideal can occur without any visual exposure to body shapes.These results may shed light on sociocultural factors that should be considered in evaluating disordered eating risk among blind women.

Charged particles in the heliosphere can be continuously accelerated by interplanetary shocks and eventually escape from these shocks without returning to it. Acceleration and escape are highly intertwined and both contribute to the shaping of the particles’ momentum spectrum at the shock. The simplest model which describes this phenomenon is called Diffusive Shock Acceleration (DSA). DSA has been very successful in describing several observations. However, DSA does not include an energy-dependent escape from the foreshock region. We expand upon DSA by presenting a model for interplanetary shock acceleration which includes this energy-dependent particle escape. We analytically solve a one-dimensional transport equation with a diffusion coefficient and an escape time that describes both the turbulence self-generated by the shock and the far upstream pre-existing turbulence. We consider the case where a shock encounters a population of pre-existing charged particles with a power law energy distribution as measured by spacecraft. We find that at lower energies our solution is concave, whereas at higher energies it asymptotically approaches a power law whose slope depends on the original energy spectrum’s power law index and shock parameters. We fitted the solution obtained from this transport equation to shock data measured from multiple shock events collected by ACE/EPAM (the Electron, Proton, and Alpha Monitor aboard the Advanced Composition Explorer spacecraft). We find that for the shock events considered, our model’s best fit parameters match very well with the predicted values, obtained by using the measured shock parameters. From this model, we can better understand the mechanism of interplanetary shock acceleration and how this phenomenon energizes charged particles near the Sun and around other objects (for example, blazars and supernova remnants).

Breast cancer has higher fatality rates in low-to-middle-income countries (LMICs) within Sub-Saharan Africa compared to more developed countries. Extensive wait times for an evaluation and lack of timely follow-up care contribute to this disparity. In LMICs, breast core needle biopsies (CNBs) are commonly taken from patients by palpation, then transferred to pathologists who manually chemically preserve, slice, and analyze the tissue, which may take weeks to months for a report. We are developing CoreView, a fast, automated, and low-cost device with the ability to assess disease status within one patient visit. The CoreView instrument accepts fresh CNBs, automatically stains tissue surfaces, and generates an optical diagnostic image. For nuclei to be imaged rapidly with high-resolution within a limited depth of focus, the CNB must be pressed against a smooth clear surface, which also maximizes the tissue surface area being analyzed. To do this, compression mechanisms were modeled in SolidWorks using a piston approach and fluidic pumps to apply positive pressure. Breast tissue has low stiffness, requiring precise, applied forces. The CNB integrity and diagnostic image quality during compression was quantitatively video monitored and studied. As a control, images of compressed porcine breast CNBs were compared to matched uncompressed tissues to determine any damage with compression, and measured improvement in diagnostic image quality. Breast CNBs are expected to withstand a maximum pressure of 1.5 psi without significant tissue deformity; however, the threshold depends on the prototype dimensions/geometry. The goal is to form high-magnification, panoramic diagnostic images along the entire length of 20 mm long CNBs with ~20x microscope objective lens within one minute using motorized stage and synchronized imaging. With a reliable design and precisely controlled compression process, CoreView allows for efficient, high-resolution tissue imaging and diagnostic analysis at the point of care, reducing health disparities through prompt breast cancer treatment.

The pairing of high-resolution volumetric imaging methods with cellular markers of neural activity holds network-level explanatory power over behavior. However, common statistical analysis approaches fall short in capturing the functional relationships between brain regions across multiple spatial dimensions. Here, we propose combining unsupervised machine learning clustering methods with network graph theory visualization to reveal intricacies from these data beyond conventional standards. We demonstrate the feasibility of this approach on a recent single-cell dataset describing the longitudinal changes of brain-wide activation during relapse to palatable food in mice. We applied a new analytical framework combining the functionality of two open-source programs: (i) Histo-Cytometric Multidimensional Analysis Pipeline (CytoMAP), packaged with unsupervised k-means clustering and t-distributed stochastic neighbor embedding (t-SNE), and (ii) Cytoscape, a network analysis program. Hierarchical radial network diagrams were applied to the dataset in which we visualized the anatomical organization of regions that underwent statistically significant changes in activation. Across abstinence duration, we found an initial suppression in activation followed by widespread increases in activation. This increase correlated with observed behavioral changes and appeared to be triggered by activation hotspots. We next interrogated the co-activational relationships amongst the >900 brain regions by running unsupervised t-SNE dimensionality reductions on the data from each experimental group. These results were validated using k-means clustering and Davies-Bouldin indexing. We observed an intuitive segregation of regions dependent on activation status. Cluster number decreased in a time-dependent manner, suggesting increases in modular processing are associated with increased activation due to abstinence length. This trend indicated that cluster membership of regions likely also changed in a time-dependent fashion, indicating a dynamic recruitment effect at a regional level underlies abstinence-related relapse vulnerability. By analyzing cellular whole-brain data in this novel manner, we gained new insight into a previously unexplored dimension of brain activation dynamics underlying complex behavior.